From human mobility to renewable energies: Big data analysis to approach worldwide multiscale phenomena

TL;DR

This paper explores how big data analysis reveals multiscale phenomena in renewable energy production and human mobility, highlighting their intermittent and scale-dependent characteristics for better understanding and management.

Contribution

It introduces a multiscale analysis approach using big data in renewable energy and human mobility, demonstrating scale-dependent behaviors and implications for stability and planning.

Findings

Wind power production is intermittent at all scales studied.

Human mobility features differ significantly across spatial scales.

Open source data can effectively reveal multiscale phenomena.

Abstract

We address and discuss recent trends in the analysis of big data sets, with the emphasis on studying multiscale phenomena. Applications of big data analysis in different scientific fields are described and two particular examples of multiscale phenomena are explored in more detail. The first one deals with wind power production at the scale of single wind turbines, the scale of entire wind farms and also at the scale of a whole country. Using open source data we show that the wind power production has an intermittent character at all those three scales, with implications for defining adequate strategies for stable energy production. The second example concerns the dynamics underlying human mobility, which presents different features at different scales. For that end, we analyze $12$-month data of the Eduroam database within Portuguese universities, and find that, at the smallest scales, typically within a set of a few adjacent buildings, the characteristic exponents of average displacements are different from the ones found at the scale of one country or one continent.